Does it make any sense to build a regression model for a certain target variable on a certain training set. Then build a regression model for the errors of the previous model ( real values vs predicted). And then add the results of both models on a test set. Has anyone heard of anything like this? I tried to do some prior research but dont know how to even begin searching for this.
To explain a little bit more, I'm trying to do an interpolation of maximum tree heights per squared KM over a certain territory. I tried with a random forest regression wich does quite well but I wanted to improve it bit if there is a chance. I have big errors (this is all on a 20% test set) when I try to predict very tall trees or very short trees. The model is biased and doesnt have enough information to "see" these big and small heights. I was looking for a way to correct this if theres is one. I uploaded an image of what is happening, I ordered the error from smallest (overestimation) to largest (subestimation). I hope it helps.